One key per team, hard budget caps, Slack alerts, provider fallbacks, semantic cache and Grafana dashboards. Bring your own provider keys.
Why it matters
of LLM calls in production error out, and 60% of those errors are rate limits.
of companies have nobody who owns AI spend.
exceed their AI budget.
Sources: Datadog LLM Observability, 2025 · FinOps Foundation State of FinOps, 2026
What you get
Monthly or daily budgets per team and app. Over the cap, the call is rejected with a clear error, not a surprise invoice.
Every call attributed to team, app, model and environment. Reports per dimension, per day.
Slack, email or webhook at 80% burn, on error spikes and on rate-limit storms — with the team, app and top models in the message.
Provider fallback chains, retries with backoff and per-key rate limits, configured by someone who has run production inference.
Exact-match and semantic caching per app. Repeated questions never reach the provider.
Spend by team, error and rate-limit map, latency percentiles, cache hit rate, budget burn-down. Exportable JSON.
How it works
OpenAI-compatible. No SDK change, no code change beyond configuration.
Give each team or app a key and a monthly cap. Pick where alerts go.
Dashboards and alerts from day one. A monthly cost review on Scale.
Bring your own provider keys — you keep paying OpenAI, Anthropic or Google directly. Logging is off by default. Need prompts to stay inside your cloud? See the In-your-cloud plan.
Works with LLM, speech and image APIs: OpenAI · Anthropic · Google · AWS Bedrock · Azure OpenAI · ElevenLabs · Deepgram · Cartesia · Replicate · fal
Pricing
Early access: the first 10 teams get 50% off for 6 months.
Early access
Tell us what you run. You will hear from us within 24 hours.
FAQ